This paper addresses the problem of multi-robot task allocation and trajectory planning in industrial environments. The objective is to optimize the overall cost of robot surveillance patrols in a dynamic high-risk environment. In this context, a hybrid beam search based approach is proposed to plan the patrol trajectories iteratively to accommodate environmental changes under some functional and operational constraints. Moreover, a real-time based system is introduced for remotely monitoring dynamic surveillance missions with automated mobile agents. Finally, a case study is detailed to show the efficiency of our approach in the case of the industrial port area of Fos-sur-Mer city in France.

This paper introduces a distributed Multi-Robot Task Allocation (MRTA) approach where each robot can autonomously decide the set of tasks it performs and determine the sequence of sites to visit while optimizing the traveling costs. During the distributed task allocation process, conflicts may arise when multiple robots are able to perform identical tasks. To address this issue, we present a consensus algorithm that combines negotiation and optimization phases. The algorithm allows each robot to decide whether to add or remove conflicting tasks based on information exchanged within the team of robots. Subsequently, we discuss the contribution through a numerical experimentation that compares ascending and descending strategies and compare the performance with a centralized optimization approach.

This paper addresses the problem of task allocation for monitoring patrols in industrial areas. The objective is to decentralize the process of task allocation so that each robot can make decisions based on its configuration and environmental information. In this context, we introduce a distributed heuristic approach based on the Hybrid Filtered Beam Search (HFBS) algorithm to determine the optimal trajectory of each robot. In order to validate our approach, a case study in the industrial port area of Le Havre city is presented. The results are therefore promising since each robot calculates its optimal trajectory using only measurements from sites it can process rather than using all environmental data.

Over the past few years, parallel and distributed processing using the MapReduce programming paradigm has gained considerable attention. The purpose of such a model is to ease parallel computing and concurrent processing of large data stored on the Hadoop platform. However, MapReduce may experience performance and timing problems while using more complex and time-consuming data processing jobs. This paper focuses on improving MapReduce's performance. As a first step, we propose optimizing MapReduce jobs. The system conformance is then tested using a distributed test application. Finally, the test process is optimized using the Markov Decision Process.

The emergence of the Internet of Things (IoT) has raised several issues related to the development and deployment of IoT applications within IT infrastructures. In this context, cloud computing is often faced with latency issues when hosting such applications, even though the cloud environment offers promising opportunities to increase productivity and reduce costs significantly. Therefore, it is important to review the current conformance scheme in the cloud environments by considering the coordination challenges during data processing. For this purpose, we suggest an architecture for conformance testing of IoT based implementations in the Cloud. The idea is to set parallel testers to handle the conformance of the distributed implementation with respect to the specification. In this case, the testing process must support coordination between the different distributed components in order to detect the resulting faults. Therefore, the main contribution of this work is to propose a new architecture based on Markov decision processes with an adaptive controller to monitor and optimize the overall testing process in the distributed Cloud.

Hadoop has emerged over the past few years as the most popular framework to process large-scale datasets efficiently. The idea is to process large datasets using several programs on multi-nodes hardware. However, the execution of such model on large clusters can be more complex and could produce several failures due to bugs or hardware problems or misuse of sensitive data. In fact, data security and Map-Reduce calculations are key concerns when a Map-Reduce calculation is performed in public domain. In this paper, we propose an Access Control model based on XACML language to test the Access Control List policies on both the MapReduce framework and the Hadoop distributed file system. To this end, we transform both the Access Control List policies and the users' objectives using the XACML language. Then, we introduce a Multi-Agents system to test the objectives expressed by users against the ACL policies.

Testing a distributed system, to ensure its conformance to the specification, involves usually placing a set of parallel testers called PTCs (Parallel Test Components) attached to each port of the implementation under test (IUT). As a result, many problems known as controllability, observability and synchronisation fault detections occur while testing such distributed systems. Hence, the design process should take into consideration the mechanisms and functions needed to support interaction, communication and coordination between the distributed components especially if the implementation exhibits a stochastic behaviour.In this paper, we use probabilistic automaton to model and analyse the stochastic behaviour of the implementation under test. Then, we propose an algorithm for generating probabilistic local test sequences (PLTS) for each tester guarantying to avoid problems of coordination, observation and synchronization during the testing process.

Nowadays, MapReduce becomes one of the most common computational paradigms that provide an efficient parallel processing of large-scale data especially in public clouds in order to enable users to process data without considering physical infrastructures and software installations. However, the deployment of MapReduce in such public environment needs to deal with many security threats attacks during networks communication. In this paper, we present a novel approach to test Access Control List (ACL) policies in MapReduce framework. The proposed system uses these ACLs to control which users can submit to certain queues as well as which users can administer a queue for example. To this end, we used the FSM formalism to write our system specification that takes into account these policies expressed in XACML language.

A crucial part of the development of distributed systems process is the test phase. Indeed, in the distributed testing context, the use of multiple testers introduces the possibility of coordination problems amongst remote testers. These potential problems are known as controllability and observability fault detections which are fundamental features of conformance in distributed testing. The paper presents some technical issues for testing such frameworks using rules based System. The proposed approach consists on exploring how a colored Petri net model used in distributed testing prototype realization contribute to design the communication between different components of the distributed test application and by the way capture the complex monitoring tasks of the distributed testers.